learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 112
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.01
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Mdl	Accumulated Loss	Correct Preds	Total Preds	Accuracy	Correct Gen Preds	Gen Accuracy	Correct Gen Preds 32	Correct Preds 32	Total Labels 32	Accuracy 32	Gen Accuracy 32	Correct Gen Preds 33	Correct Preds 33	Total Labels 33	Accuracy 33	Gen Accuracy 33	Correct Gen Preds 34	Correct Preds 34	Total Labels 34	Accuracy 34	Gen Accuracy 34	Correct Gen Preds 35	Correct Preds 35	Total Labels 35	Accuracy 35	Gen Accuracy 35
No log	0	0	1.5354	0.0058	1262.6022	875.1692	172.0	570.0	0.3018	170.0	0.2982	154.0	154.0	158.0	0.9747	0.9747	0.0	0.0	152.0	0.0	0.0	15.0	17.0	142.0	0.1197	0.1056	1.0	1.0	118.0	0.0085	0.0085
0.7537	1.0	7	1.0766	0.0058	885.2932	613.6385	375.0	570.0	0.6579	375.0	0.6579	128.0	128.0	158.0	0.8101	0.8101	93.0	93.0	152.0	0.6118	0.6118	90.0	90.0	142.0	0.6338	0.6338	64.0	64.0	118.0	0.5424	0.5424
0.521	2.0	14	1.0121	0.0058	832.3148	576.9166	402.0	570.0	0.7053	402.0	0.7053	101.0	101.0	158.0	0.6392	0.6392	120.0	120.0	152.0	0.7895	0.7895	110.0	110.0	142.0	0.7746	0.7746	71.0	71.0	118.0	0.6017	0.6017
0.1506	3.0	21	1.2469	0.0058	1025.3714	710.7333	381.0	570.0	0.6684	380.0	0.6667	93.0	94.0	158.0	0.5949	0.5886	111.0	111.0	152.0	0.7303	0.7303	91.0	91.0	142.0	0.6408	0.6408	85.0	85.0	118.0	0.7203	0.7203
0.0034	4.0	28	2.0883	0.0058	1717.2546	1190.3102	387.0	570.0	0.6789	387.0	0.6789	105.0	105.0	158.0	0.6646	0.6646	113.0	113.0	152.0	0.7434	0.7434	98.0	98.0	142.0	0.6901	0.6901	71.0	71.0	118.0	0.6017	0.6017
0.0001	5.0	35	3.1970	0.0058	2628.9738	1822.2658	385.0	570.0	0.6754	385.0	0.6754	106.0	106.0	158.0	0.6709	0.6709	109.0	109.0	152.0	0.7171	0.7171	101.0	101.0	142.0	0.7113	0.7113	69.0	69.0	118.0	0.5847	0.5847
0.0215	6.0	42	3.4074	0.0058	2802.0180	1942.2109	382.0	570.0	0.6702	382.0	0.6702	96.0	96.0	158.0	0.6076	0.6076	114.0	114.0	152.0	0.75	0.75	109.0	109.0	142.0	0.7676	0.7676	63.0	63.0	118.0	0.5339	0.5339
0.0001	7.0	49	3.6803	0.0058	3026.4624	2097.7839	382.0	570.0	0.6702	378.0	0.6632	87.0	87.0	158.0	0.5506	0.5506	116.0	116.0	152.0	0.7632	0.7632	109.0	109.0	142.0	0.7676	0.7676	66.0	70.0	118.0	0.5932	0.5593
0.0001	8.0	56	3.6821	0.0058	3027.9010	2098.7811	385.0	570.0	0.6754	383.0	0.6719	95.0	95.0	158.0	0.6013	0.6013	110.0	110.0	152.0	0.7237	0.7237	108.0	108.0	142.0	0.7606	0.7606	70.0	72.0	118.0	0.6102	0.5932
0.0	9.0	63	3.8801	0.0058	3190.7166	2211.6362	386.0	570.0	0.6772	385.0	0.6754	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	107.0	107.0	142.0	0.7535	0.7535	74.0	75.0	118.0	0.6356	0.6271
0.0001	10.0	70	3.9489	0.0058	3247.2899	2250.8498	386.0	570.0	0.6772	385.0	0.6754	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	107.0	107.0	142.0	0.7535	0.7535	74.0	75.0	118.0	0.6356	0.6271
0.0	11.0	77	4.0069	0.0058	3295.0467	2283.9523	386.0	570.0	0.6772	385.0	0.6754	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	107.0	107.0	142.0	0.7535	0.7535	74.0	75.0	118.0	0.6356	0.6271
0.0	12.0	84	4.0100	0.0058	3297.5344	2285.6767	386.0	570.0	0.6772	385.0	0.6754	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	107.0	107.0	142.0	0.7535	0.7535	74.0	75.0	118.0	0.6356	0.6271
0.0	13.0	91	4.0224	0.0058	3307.7362	2292.7480	387.0	570.0	0.6789	387.0	0.6789	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	108.0	108.0	142.0	0.7606	0.7606	75.0	75.0	118.0	0.6356	0.6356
0.0	14.0	98	4.0302	0.0058	3314.2083	2297.2341	387.0	570.0	0.6789	386.0	0.6772	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	108.0	108.0	142.0	0.7606	0.7606	74.0	75.0	118.0	0.6356	0.6271
0.0	15.0	105	4.0472	0.0058	3328.1717	2306.9128	387.0	570.0	0.6789	386.0	0.6772	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	108.0	108.0	142.0	0.7606	0.7606	74.0	75.0	118.0	0.6356	0.6271
0.0	16.0	112	4.0484	0.0058	3329.1411	2307.5847	387.0	570.0	0.6789	385.0	0.6754	94.0	95.0	158.0	0.6013	0.5949	110.0	110.0	152.0	0.7237	0.7237	107.0	107.0	142.0	0.7535	0.7535	74.0	75.0	118.0	0.6356	0.6271
0.0	17.0	119	4.0769	0.0058	3352.6059	2323.8493	387.0	570.0	0.6789	386.0	0.6772	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	108.0	108.0	142.0	0.7606	0.7606	74.0	75.0	118.0	0.6356	0.6271
0.0	18.0	126	4.0711	0.0058	3347.7892	2320.5106	385.0	570.0	0.6754	384.0	0.6737	93.0	93.0	158.0	0.5886	0.5886	110.0	110.0	152.0	0.7237	0.7237	107.0	107.0	142.0	0.7535	0.7535	74.0	75.0	118.0	0.6356	0.6271
0.0	19.0	133	4.0933	0.0058	3366.1069	2333.2075	387.0	570.0	0.6789	386.0	0.6772	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	108.0	108.0	142.0	0.7606	0.7606	74.0	75.0	118.0	0.6356	0.6271
0.0	20.0	140	4.0916	0.0058	3364.6355	2332.1876	388.0	570.0	0.6807	387.0	0.6789	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	109.0	109.0	142.0	0.7676	0.7676	74.0	75.0	118.0	0.6356	0.6271
0.0	21.0	147	4.0825	0.0058	3357.2105	2327.0410	388.0	570.0	0.6807	387.0	0.6789	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	109.0	109.0	142.0	0.7676	0.7676	74.0	75.0	118.0	0.6356	0.6271
0.0	22.0	154	4.0640	0.0058	3341.9560	2316.4674	387.0	570.0	0.6789	386.0	0.6772	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	108.0	108.0	142.0	0.7606	0.7606	74.0	75.0	118.0	0.6356	0.6271
0.0	23.0	161	4.0841	0.0058	3358.4668	2327.9118	388.0	570.0	0.6807	387.0	0.6789	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	109.0	109.0	142.0	0.7676	0.7676	74.0	75.0	118.0	0.6356	0.6271
0.0	24.0	168	4.0792	0.0058	3354.4495	2325.1272	388.0	570.0	0.6807	386.0	0.6772	94.0	95.0	158.0	0.6013	0.5949	110.0	110.0	152.0	0.7237	0.7237	108.0	108.0	142.0	0.7606	0.7606	74.0	75.0	118.0	0.6356	0.6271
0.0	25.0	175	4.0917	0.0058	3364.7455	2332.2639	387.0	570.0	0.6789	386.0	0.6772	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	108.0	108.0	142.0	0.7606	0.7606	74.0	75.0	118.0	0.6356	0.6271
0.0	26.0	182	4.1026	0.0058	3373.7140	2338.4803	387.0	570.0	0.6789	386.0	0.6772	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	108.0	108.0	142.0	0.7606	0.7606	74.0	75.0	118.0	0.6356	0.6271
0.0	27.0	189	4.1036	0.0058	3374.5095	2339.0317	387.0	570.0	0.6789	386.0	0.6772	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	108.0	108.0	142.0	0.7606	0.7606	74.0	75.0	118.0	0.6356	0.6271
0.0	28.0	196	4.1163	0.0058	3385.0067	2346.3078	387.0	570.0	0.6789	386.0	0.6772	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	108.0	108.0	142.0	0.7606	0.7606	74.0	75.0	118.0	0.6356	0.6271
0.0	29.0	203	4.1011	0.0058	3372.5132	2337.6480	387.0	570.0	0.6789	386.0	0.6772	95.0	95.0	158.0	0.6013	0.6013	110.0	110.0	152.0	0.7237	0.7237	107.0	107.0	142.0	0.7535	0.7535	74.0	75.0	118.0	0.6356	0.6271
0.0	30.0	210	4.1281	0.0058	3394.7200	2353.0406	387.0	570.0	0.6789	386.0	0.6772	95.0	95.0	158.0	0.6013	0.6013	110.0	110.0	152.0	0.7237	0.7237	107.0	107.0	142.0	0.7535	0.7535	74.0	75.0	118.0	0.6356	0.6271
0.0	31.0	217	4.1409	0.0058	3405.1901	2360.2979	387.0	570.0	0.6789	385.0	0.6754	94.0	95.0	158.0	0.6013	0.5949	109.0	109.0	152.0	0.7171	0.7171	108.0	108.0	142.0	0.7606	0.7606	74.0	75.0	118.0	0.6356	0.6271
0.0	32.0	224	4.1452	0.0058	3408.7242	2362.7476	387.0	570.0	0.6789	386.0	0.6772	94.0	94.0	158.0	0.5949	0.5949	110.0	110.0	152.0	0.7237	0.7237	108.0	108.0	142.0	0.7606	0.7606	74.0	75.0	118.0	0.6356	0.6271

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

donoway/ARC-Easy_Llama-3.2-1B-l00pih28

ARC-Easy_Llama-3.2-1B-l00pih28

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
ARC-Easy_Llama-3.2-1B-l00pih28